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Record W2963334706 · doi:10.1002/jia2.25292

Enhancing an <scp>HIV</scp> index case testing passive referral model through a behavioural skills‐building training for healthcare providers: a pre‐/post‐assessment in Mangochi District, Malawi

2019· article· en· W2963334706 on OpenAlexaboutno aff
Tapiwa Tembo, Maria H. Kim, Katherine Simon, Saeed Ahmed, Teferi Beyene, Elizabeth Wetzel, Mphatso Machika, Chrissy Chikoti, Willy Kammera, Henry Chibowa, Zinaumaleka Nkhono, Elijah Kavuta, Peter N. Kazembe, Nora E. Rosenberg

Bibliographic record

VenueJournal of the International AIDS Society · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersFogarty International CenterNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthU.S. President’s Emergency Plan for AIDS Relief
KeywordsMedicineReferralPartner notificationFamily medicineQuarter (Canadian coin)Index (typography)Health careHuman immunodeficiency virus (HIV)Health facilityReproductive healthNursingEnvironmental healthPopulationHealth services

Abstract

fetched live from OpenAlex

INTRODUCTION: Although knowledge of HIV positivity is a necessary step towards engagement in HIV care, more than one quarter of HIV-positive Malawians remain unaware of their HIV status. Testing the sexual partners, guardians and children of HIV-positive persons (index case finding or ICF) is a promising way of identifying HIV-positive persons unaware of their HIV status. ICF can be passive where the HIV-positive individual (index) invites a partner (or contact) for HIV testing or active where a health provider assists the index with partner notification and offers HIV testing to the partner. Strategies to improve passive ICF have not been thoroughly studied. We describe the impact of a behavioural skills-building training to enhance healthcare workers' (HCWs) implementation of Malawi's passive ICF programme. METHODS: In June 2017, HCWs from 36 health facilities in Mangochi were oriented to Malawi's ICF programme and began implementation. In February and April 2018, a total of 573 HCWs from these facilities received further training from the Tingathe Programme. The training focused on eliciting more untested sexual contacts from indexes and better equipping indexes on issuing "family referral slips" to contacts. Monthly programmatic data were abstracted from clinical registers from October 2017 to July 2018. Monthly programmatic indicators were collected from the Index Case Testing Register and the HIV Counselling and Testing Register and were entered into a data set with one record per facility per month. T-tests were used to compare the means of these indicators. RESULTS: During the ten-month study period, there were 200 facility-months observed before and 124 facility-months observed after training. The mean number of indexes identified per facility-month remained stable after training (pre = 18.9, post = 21.2, p = 0.74), but the mean number of sexual partners listed per facility-month (pre = 6.3, post = 10.6, p < 0.001) increased. The mean number of contacts who received HIV testing (pre = 11.1, post = 24.8, p < 0.001) and the mean number of HIV-positive contacts identified per facility-month (pre = 1.3, post = 2.3, p < 0.001) also increased. CONCLUSIONS: A brief behavioural skills-building training impacted a range of meaningful outcomes, including identification of HIV-positive individuals in a passive ICF programme. Such approaches could facilitate the identification of HIV-positive persons unaware of their HIV status, a necessary step for engagement in HIV care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.400
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2019
Admission routes1
Has abstractyes

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